Quantum Machine Learning: Algorithms and Complexities
Abstract:
This article provides a comprehensive overview of QML algorithms and explores their complexities. It explores the characteristics of quantum data, hybrid quantum-classical models, variational quantum algorithms, quantum-enhanced reinforcement learning, and the difficulties associated with quantum machine learning. Overall, this article provides a valuable resource for researchers and practitioners interested in understanding the algorithms, complexities, and potential of Quantum Machine Learning, shedding light on its current state and future prospects.
Introduction:
Quantum machine learning, also known as QML, is a blooming field of modern artificial intelligence that integrates quantum computing with machine learning. It aims to enhance traditional machine learning algorithms and develop novel computational methods.
This article examines the inner workings of quantum machine learning and related topics. It includes the fundamentals of quantum computing, quantum machine learning algorithms, the characteristics of quantum data, hybrid quantum-classical models, variational quantum algorithms, quantum-enhanced reinforcement learning, and the difficulties associated with quantum machine learning.
“The fusion of quantum computing and artificial intelligence, paving the way for groundbreaking innovation and endless opportunities.” – Sri Amit Ray
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